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home / papers / 2023 / DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Written for the Research library (claude-opus-5-5) from the paper text. Original — arxiv.org/pdf/2306.11698

Summary

Practitioners increasingly propose GPT models for high-stakes domains such as healthcare and finance, yet little systematic evidence exists on how reliable these models are. DecodingTrust addresses this gap with a broad benchmark that examines GPT-3.5 and GPT-4 across eight dimensions: privacy, fairness, machine ethics, toxicity, bias rooted in stereotypes, resilience against adversarial inputs, behavior under out-of-distribution data, and resistance to adversarially crafted demonstrations. The authors combine existing benchmarks with newly built resources, including hand-crafted adversarial system prompts, model-generated difficult user prompts, a stereotype statement dataset, and a harder variant of AdvGLUE.

The evaluation reveals weaknesses that had not been documented before. Both models can be steered into producing toxic or biased text. Both can also expose private information, whether it comes from their training data or from earlier turns in a conversation. GPT-4 generally scores better than GPT-3.5 on standard tests. However, it is easier to manipulate with jailbreaking prompts, plausibly because it obeys instructions more faithfully, including harmful ones. The released benchmark gives practitioners a reusable tool for checking model risks before deployment.

Key points

  • Evaluation targets fixed model snapshots for reproducibility: GPT-3.5 (March 1, 2023) and GPT-4 (March 14, 2023). An appendix extends the evaluation to open LLMs such as llama, Llama 2, Alpaca and Red Pajama.
  • The toxicity tests use three resources:
    • REALTOXICITYPROMPTS;
    • 33 manually designed system prompts, using tricks like role-playing and inverted word meanings;
    • about 1.2K challenging user prompts generated by GPT-3.5 and GPT-4.
  • Robustness is measured with AdvGLUE and the new AdvGLUE++. Privacy leakage is probed with the Enron email dataset and with PII injected into conversations.
  • Proposed future work includes multi-turn interactive evaluation, coordinated adversaries, domain-specific testing, formal verification, and safeguards based on game theory or added knowledge.
  • Published at the NeurIPS 2023 Datasets and Benchmarks track. Data is available at huggingface.co/datasets/AI-Secure/DecodingTrust.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing · AI & machine learning

Cited by

160 papers of our library cite it (of the 140673 whose bibliographies we read); 8 of them have a page here, 152 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2306.11698 · arXiv 2306.11698

@misc{wang2023decodingtrust,
  title = {{DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models}},
  author = {Wang, Boxin and Chen, Weixin and Pei, Hengzhi and Xie, Chulin and Kang, Mintong and Zhang, Chenhui and Xu, Chejian and Xiong, Zidi and Dutta, Ritik and Schaeffer, Rylan and Truong, Sang T. and Arora, Simran and Mazeika, Mantas and Hendrycks, Dan and Lin, Zinan and Cheng, Yu and Koyejo, Sanmi and Song, Dawn and Li, Bo},
  year = {2023},
  doi = {10.48550/arxiv.2306.11698},
  eprint = {2306.11698},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2306.11698}
}

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